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IDEA_DC_chpt.py
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IDEA_DC_chpt.py
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import torch
from utils import *
import scipy.sparse
import random
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
setup_seed(0)
# ====================
data_name = 'DC'
num_nodes = 128 # Number of nodes
num_snaps = 700 # Number of snapshots
max_thres = 5000 # Threshold for maximum edge weight
noise_dim = 100 # Dimension of noise input
feat_dim = 32 # Dimension of node feature
pos_dim = 32 # Dimension of position embedding
# ====================
edge_seq = np.load('data/%s_edge_seq.npy' % (data_name), allow_pickle=True)
mod_seq = np.load('data/%s_mod_seq.npy' % (data_name), allow_pickle=True)
# ==========
# Get node features
feat = np.load('data/%s_feat.npy' % (data_name), allow_pickle=True)
feat_tnr = torch.FloatTensor(feat).to(device)
# Get positional embedding
pos_emb = None
for p in range(num_nodes):
if p==0:
pos_emb = get_pos_emb(p, pos_dim)
else:
pos_emb = np.concatenate((pos_emb, get_pos_emb(p, pos_dim)), axis=0)
pos_tnr = torch.FloatTensor(pos_emb).to(device)
feat_tnr = torch.cat((feat_tnr, pos_tnr), dim=1)
# ====================
win_size = 5 # Window size of historical snapshots
lambd = 0.1 # Hyper-parameter of attentive aligning unit
epsilon = 0.01 # Threshold of the zero-refining
num_test_snaps = 50 # Number of test snapshots
num_val_snaps = 10 # Number of validation snapshots
num_train_snaps = num_snaps-num_test_snaps-num_val_snaps # Number of training snapshots
# ====================
# Get align matrics
align_mat = torch.eye(num_nodes).to(device)
align_list = []
for i in range(win_size):
align_list.append(align_mat)
# ==========
feat_list = []
for i in range(win_size+1):
feat_list.append(feat_tnr)
# ==========
num_nodes_list = []
for i in range(win_size+1):
num_nodes_list.append(num_nodes)
# ====================
# Load check point
gen_net = torch.load('chpt/IDEA_%s.pkl' % (data_name)).to(device)
# ====================
# Evaluate the model on the test set
gen_net.eval()
# ==========
RMSE_list = []
MAE_list = []
MLSD_list = []
MR_list = []
for tau in range(num_snaps-num_test_snaps, num_snaps):
# ====================
sup_list = [] # List of GNN support (tensor)
noise_list = [] # List of random noise inputs
for t in range(tau-win_size, tau):
# ==========
edges = edge_seq[t]
adj = get_adj_wei(edges, num_nodes, max_thres)
adj_norm = adj/max_thres # Normalize the edge weights to [0, 1]
# ==========
# Transfer the GNN support to a sparse tensor
sup = get_gnn_sup(adj_norm)
sup_sp = sp.sparse.coo_matrix(sup)
sup_sp = sparse_to_tuple(sup_sp)
idxs = torch.LongTensor(sup_sp[0].astype(float)).to(device)
vals = torch.FloatTensor(sup_sp[1]).to(device)
sup_tnr = torch.sparse.FloatTensor(idxs.t(), vals, sup_sp[2]).float().to(device)
sup_list.append(sup_tnr)
# =========
# Generate the random noise via random projection
mod_tnr = torch.FloatTensor(mod_seq[t]).to(device)
rand_mat = rand_proj(num_nodes, noise_dim)
rand_tnr = torch.FloatTensor(rand_mat).to(device)
noise_tnr = torch.mm(mod_tnr, rand_tnr)
noise_list.append(noise_tnr)
# ==========
# Get the prediction result
adj_est_list = gen_net(sup_list, feat_list, noise_list, align_list, num_nodes_list, lambd, pred_flag=True)
adj_est = adj_est_list[-1]
if torch.cuda.is_available():
adj_est = adj_est.cpu().data.numpy()
else:
adj_est = adj_est.data.numpy()
adj_est *= max_thres # Rescale the edge weights to the original value range
# ==========
# Refine the prediction result
for r in range(num_nodes):
adj_est[r, r] = 0
for r in range(num_nodes):
for c in range(num_nodes):
if adj_est[r, c] <= epsilon:
adj_est[r, c] = 0
# ====================
# Get ground-truth
edges = edge_seq[tau]
gnd = get_adj_wei(edges, num_nodes, max_thres)
# ====================
# Evaluate the prediction result
RMSE = get_RMSE(adj_est, gnd, num_nodes)
MAE = get_MAE(adj_est, gnd, num_nodes)
MLSD = get_MLSD(adj_est, gnd, num_nodes)
MR = get_MR(adj_est, gnd, num_nodes)
# ==========
RMSE_list.append(RMSE)
MAE_list.append(MAE)
MLSD_list.append(MLSD)
MR_list.append(MR)
# ====================
RMSE_mean = np.mean(RMSE_list)
RMSE_std = np.std(RMSE_list, ddof=1)
MAE_mean = np.mean(MAE_list)
MAE_std = np.std(MAE_list, ddof=1)
MLSD_mean = np.mean(MLSD_list)
MLSD_std = np.std(MLSD_list, ddof=1)
MR_mean = np.mean(MR_list)
MR_std = np.std(MR_list, ddof=1)
print('Test RMSE %f %f MAE %f %f MLSD %f %f MR %f %f\n'
% (RMSE_mean, RMSE_std, MAE_mean, MAE_std, MLSD_mean, MLSD_std, MR_mean, MR_std))